mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 18:45:21 +08:00
3a08db9182
* add mytrain.py for test * test before layers * test attr in layers * test classifier * delete mytrain.py * add rand_bbox_minmax rand_bbox and cutmix_bbox_and_lam to BaseCutMixLayer * add mixup_prob to BatchMixupLayer * add cutmixup * add cutmixup to __init__ * test classifier with cutmixup * delete some comments * set mixup_prob default to 1.0 * add cutmixup to classifier * use cutmixup * use cutmixup * fix bugs * test cutmixup * move mixup and cutmix to augment * inherit from BaseAugment * add BaseAugment * inherit from BaseAugment * rename identity.py * add @ * build augment * register module * rename to augment.py * delete cutmixup.py * do not inherit from BaseAugment * add augments * use augments in classifier * prob default to 1.0 * add comments * use augments * use augments * assert sum of augmentation probabilities should equal to 1 * augmentation probabilities equal to 1 * calculate Identity prob * replace xxx with self.xxx * add comments * sync with augments * for BC-breaking * delete useless comments in mixup.py
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
from abc import ABCMeta, abstractmethod
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
|
|
from .builder import AUGMENT
|
|
|
|
|
|
class BaseMixupLayer(object, metaclass=ABCMeta):
|
|
"""Base class for MixupLayer.
|
|
|
|
Args:
|
|
alpha (float): Parameters for Beta distribution.
|
|
num_classes (int): The number of classes.
|
|
prob (float): MixUp probability. It should be in range [0, 1].
|
|
Default to 1.0
|
|
"""
|
|
|
|
def __init__(self, alpha, num_classes, prob=1.0):
|
|
super(BaseMixupLayer, self).__init__()
|
|
|
|
assert isinstance(alpha, float) and alpha > 0
|
|
assert isinstance(num_classes, int)
|
|
assert isinstance(prob, float) and 0.0 <= prob <= 1.0
|
|
|
|
self.alpha = alpha
|
|
self.num_classes = num_classes
|
|
self.prob = prob
|
|
|
|
@abstractmethod
|
|
def mixup(self, imgs, gt_label):
|
|
pass
|
|
|
|
|
|
@AUGMENT.register_module(name='BatchMixup')
|
|
class BatchMixupLayer(BaseMixupLayer):
|
|
"""Mixup layer for batch mixup."""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super(BatchMixupLayer, self).__init__(*args, **kwargs)
|
|
|
|
def mixup(self, img, gt_label):
|
|
one_hot_gt_label = F.one_hot(gt_label, num_classes=self.num_classes)
|
|
lam = np.random.beta(self.alpha, self.alpha)
|
|
batch_size = img.size(0)
|
|
index = torch.randperm(batch_size)
|
|
|
|
mixed_img = lam * img + (1 - lam) * img[index, :]
|
|
mixed_gt_label = lam * one_hot_gt_label + (
|
|
1 - lam) * one_hot_gt_label[index, :]
|
|
|
|
return mixed_img, mixed_gt_label
|
|
|
|
def __call__(self, img, gt_label):
|
|
return self.mixup(img, gt_label)
|